TY - CPAPER
AU - Erdem Çakan
AU - Aral Şahin
AU - Mert Nakip
AU - Volkan Rodoplu
AB - <p>We develop a Multi-Layer Perceptron (MLP) Decomposition<br />
architecture for mobile Internet Things (IoT) indoor<br />
positioning. We demonstrate the performance of our architecture<br />
on an indoor system that utilizes ultra-wideband (UWB) positioning.<br />
Our architecture outperforms the following benchmark<br />
processing techniques on the same data: MLP, Linear Regression,<br />
Ridge Regression, Support Vector Regression, and the Least<br />
Squares Method for indoor positioning. The results show that our<br />
architecture can significantly advance the positioning accuracy<br />
of indoor positioning systems and enable indoor applications<br />
such as navigation, proximity marketing, asset tracking, collision<br />
avoidance, and social distancing.</p>

BT - 7th IEEE World Forum on the Internet of Things
DO - 10.1109/WF-IoT51360.2021.9595282
LA - eng
N2 - <p>We develop a Multi-Layer Perceptron (MLP) Decomposition<br />
architecture for mobile Internet Things (IoT) indoor<br />
positioning. We demonstrate the performance of our architecture<br />
on an indoor system that utilizes ultra-wideband (UWB) positioning.<br />
Our architecture outperforms the following benchmark<br />
processing techniques on the same data: MLP, Linear Regression,<br />
Ridge Regression, Support Vector Regression, and the Least<br />
Squares Method for indoor positioning. The results show that our<br />
architecture can significantly advance the positioning accuracy<br />
of indoor positioning systems and enable indoor applications<br />
such as navigation, proximity marketing, asset tracking, collision<br />
avoidance, and social distancing.</p>

PB - IEEE
PY - 2021
T2 - 7th IEEE World Forum on the Internet of Things
TI - Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning
ER -